1 Processed data

This is an interactive table of the covariate data.

2 Normalisation quality control metrics

2.1 Principal component analysis

The principal component analysis plot shown below was generated using the most varying 500 genes across all samples. The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design.

In presence of strong biological signal, the samples should cluster with the biological condition. When samples are clustered according to other effects (for example patient, or technical batch), great care must be used when interpreting the results, as the other effects will considerably reduce the ability to extract meaningful biological information.

Download plot

2.2 Hierarchical clustering

The hierarchical clustering shown below was generated using the most varying 500 genes across all samples. The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.

The hierarchical clustering can provide clues on which groups of genes could affect the clustering of samples.

Download plot

2.3 Sample similarity

The hierarchical clustering shown below was generated using all the full normalised dataset (21264 genes). The expression values are obtained by the “vst” method, where the normalisation doesn’t take into account the experimental design. The clustering is using euclidian distance for both the rows (genes) and columns (samples). In both cases, the distance between clusters is defined as the maximum of the distances between elements pairs from each cluster.

Download plot

2.4 Normalised expression densities

The expression values are obtained by the “vst” method, where the experimental design has been used for normalisation.

Download plot

2.5 DESeq2 normalisation

## Warning: Computation failed in `stat_binhex()`:
## Package `hexbin` required for `stat_binhex`.
## Please install and try again.

## Warning: Computation failed in `stat_binhex()`:
## Package `hexbin` required for `stat_binhex`.
## Please install and try again.

Download plot

2.6 Cox outliers

## Warning: Removed 62780 rows containing non-finite values (stat_boxplot).

## Warning: Removed 62780 rows containing non-finite values (stat_boxplot).

Download plot

3 Contrasts

Contrasts generated by the pipeline.

3.1 NHBE_SC2V

3.1.1 MA plot

A MA plot of the contrast NHBE_SC2V.

3.1.2 Results table

An interactive data table of the contrast results for NHBE_SC2V.

3.1.3 `tmod` enrichment analysis for NHBE_SC2V

Table. Summary of the results for contrast NHBE_SC2V shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB	0.01	0.001	1e-04	1e-06
msigdb_reactome	178	127	71	27
msigdb_hallmark	29	27	25	21
msigdb_mir	12	6	3	0
msigdb_go_bp	301	126	64	23
tmod	69	47	29	16

Table. Results of the tmod enrichment analysis for contrast NHBE_SC2V. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval

msigdb_hallmark.pval

msigdb_mir.pval

msigdb_go_bp.pval

tmod.pval

3.1.4 cluster profiler results

3.1.4.1 Dot plot

Dot plot for cluster profiler results for contrast NHBE_SC2V.

MSigDb

H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO

BP

MF

KEGG

pathways

3.1.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast NHBE_SC2V.

MSigDb

H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO

BP

MF

KEGG

pathways

3.1.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast NHBE_SC2V.

MSigDb

H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO

BP

MF

KEGG

pathways

3.2 A549_SC2V

3.2.1 MA plot

A MA plot of the contrast A549_SC2V.

3.2.2 Results table

An interactive data table of the contrast results for A549_SC2V.

3.2.3 `tmod` enrichment analysis for A549_SC2V

Table. Summary of the results for contrast A549_SC2V shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB	0.01	0.001	1e-04	1e-06
msigdb_reactome	172	96	53	25
msigdb_hallmark	18	18	18	14
msigdb_mir	18	12	3	1
msigdb_go_bp	240	143	83	35
tmod	46	29	22	10

Table. Results of the tmod enrichment analysis for contrast A549_SC2V. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval

msigdb_hallmark.pval

msigdb_mir.pval

msigdb_go_bp.pval

tmod.pval

3.2.4 cluster profiler results

3.2.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_SC2V.

MSigDb

H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO

BP

MF

KEGG

pathways

3.2.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_SC2V.

MSigDb

H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO

BP

MF

KEGG

pathways

3.2.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_SC2V.

MSigDb

H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO

BP

MF

KEGG

pathways

3.3 A549_RSV

3.3.1 MA plot

A MA plot of the contrast A549_RSV.

3.3.2 Results table

An interactive data table of the contrast results for A549_RSV.

3.3.3 `tmod` enrichment analysis for A549_RSV

Table. Summary of the results for contrast A549_RSV shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB	0.01	0.001	1e-04	1e-06
msigdb_reactome	255	154	99	49
msigdb_hallmark	32	32	31	27
msigdb_mir	23	16	14	6
msigdb_go_bp	451	246	165	90
tmod	85	48	32	21

Table. Results of the tmod enrichment analysis for contrast A549_RSV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval

msigdb_hallmark.pval

msigdb_mir.pval

msigdb_go_bp.pval

tmod.pval

3.3.4 cluster profiler results

3.3.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_RSV.

MSigDb

H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO

BP

MF

KEGG

pathways

3.3.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_RSV.

MSigDb

H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO

BP

MF

KEGG

pathways

3.3.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_RSV.

MSigDb

H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO

BP

MF

KEGG

pathways

3.4 A549_IAV

3.4.1 MA plot

A MA plot of the contrast A549_IAV.

3.4.2 Results table

An interactive data table of the contrast results for A549_IAV.

3.4.3 `tmod` enrichment analysis for A549_IAV

Table. Summary of the results for contrast A549_IAV shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB	0.01	0.001	1e-04	1e-06
msigdb_reactome	235	145	81	36
msigdb_hallmark	25	24	22	18
msigdb_mir	39	33	27	16
msigdb_go_bp	385	231	175	92
tmod	63	38	23	11

Table. Results of the tmod enrichment analysis for contrast A549_IAV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval

msigdb_hallmark.pval

msigdb_mir.pval

msigdb_go_bp.pval

tmod.pval

3.4.4 cluster profiler results

3.4.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_IAV.

MSigDb

H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO

BP

MF

KEGG

pathways

3.4.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_IAV.

MSigDb

H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO

BP

MF

KEGG

pathways

3.4.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_IAV.

MSigDb

H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO

BP

MF

KEGG

pathways

3.5 A549_SC2V_vs_IAV

3.5.1 MA plot

A MA plot of the contrast A549_SC2V_vs_IAV.

3.5.2 Results table

An interactive data table of the contrast results for A549_SC2V_vs_IAV.

## Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/
## DT/server.html

3.5.3 `tmod` enrichment analysis for A549_SC2V_vs_IAV

Table. Summary of the results for contrast A549_SC2V_vs_IAV shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB	0.01	0.001	1e-04	1e-06
msigdb_reactome	197	100	66	23
msigdb_hallmark	22	20	20	18
msigdb_mir	83	72	63	45
msigdb_go_bp	430	239	172	88
tmod	75	50	22	6

Table. Results of the tmod enrichment analysis for contrast A549_SC2V_vs_IAV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval

msigdb_hallmark.pval

msigdb_mir.pval

msigdb_go_bp.pval

tmod.pval

3.5.4 cluster profiler results

3.5.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_SC2V_vs_IAV.

MSigDb

H

C2

Error in str_count(res$core_enrichment, “/”) + 1 : non-numeric argument to binary operator

GO

BP

MF

KEGG

pathways

3.5.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_SC2V_vs_IAV.

MSigDb

H

C2

Error in emapplot.enrichResult(x, showCategory = showCategory, color = color, : no enriched term found…

GO

BP

MF

KEGG

pathways

3.5.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_SC2V_vs_IAV.

MSigDb

H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO

BP

MF

KEGG

pathways

3.6 A549_SC2V_vs_RSV

3.6.1 MA plot

A MA plot of the contrast A549_SC2V_vs_RSV.

3.6.2 Results table

An interactive data table of the contrast results for A549_SC2V_vs_RSV.

## Warning in instance$preRenderHook(instance): It seems your data is too big for client-side DataTables. You may consider server-side processing: https://rstudio.github.io/
## DT/server.html

3.6.3 `tmod` enrichment analysis for A549_SC2V_vs_RSV

Table. Summary of the results for contrast A549_SC2V_vs_RSV shows number of significant gene sets at various significance levels and for AUC > 0.65.

DB	0.01	0.001	1e-04	1e-06
msigdb_reactome	342	230	147	81
msigdb_hallmark	32	31	29	27
msigdb_mir	54	47	38	24
msigdb_go_bp	679	439	300	188
tmod	116	79	56	29

Table. Results of the tmod enrichment analysis for contrast A549_SC2V_vs_RSV. Only significantly enriched gene sets are shown (FDR < 0.01, AUC > 0.65). AUC, area under curve; p.value, p-value from enrichment test; FDR, p-value corrected for multiple testing with Benjamini-Hochberg method.

msigdb_reactome.pval

msigdb_hallmark.pval

msigdb_mir.pval

msigdb_go_bp.pval

tmod.pval

3.6.4 cluster profiler results

3.6.4.1 Dot plot

Dot plot for cluster profiler results for contrast A549_SC2V_vs_RSV.

MSigDb

H

C2

GO

BP

MF

KEGG

pathways

3.6.4.2 Enrichment map

Enrichment map for cluster profiler results for contrast A549_SC2V_vs_RSV.

MSigDb

H

C2

GO

BP

MF

KEGG

pathways

3.6.4.3 UpSet plot

UpSet plot for cluster profiler results for contrast A549_SC2V_vs_RSV.

MSigDb

H

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

C2

Error in (function (classes, fdef, mtable) : unable to find an inherited method for function ‘upsetplot’ for signature ‘“gseaResult”’

GO

BP

MF

KEGG

pathways

4 Functional analysis

4.1 Gene set enrichment analysis with tmod

4.1.1 Overview

Table. Overview of the databases for which gene set enrichment using tmod was performed.

ID	Name	Description	TaxonID	N
msigdb_reactome	Reactome gene sets (MSigDB)	Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/).	9606	1499
msigdb_hallmark	Hallmark gene sets (MSigDB)	Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/).	9606	50
msigdb_mir	MIR targets (MSigDB)	MIR targets from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/).	9606	221
msigdb_go_bp	GO Biological Process (MSigDB)	GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/).	9606	7350
tmod	Co-expression gene sets (tmod)	Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information.	9606	606

4.2 tmod enrichment analysis results for database Reactome gene sets (MSigDB).

4.2.1 Summary

Database ID: msigdb_reactome.

Description: Reactome gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast	0.05	0.01	0.001	1e-05
NHBE_SC2V_ID0.pval	453	299	204	84
A549_SC2V_ID1.pval	399	257	140	59
A549_RSV_ID2.pval	586	354	215	91
A549_IAV_ID3.pval	493	310	190	63
A549_SC2V_vs_IAV_ID4.pval	505	287	157	67
A549_SC2V_vs_RSV_ID5.pval	645	444	280	138

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

4.2.2 Figure

Fig. Panel plot showing results for the database msigdb_reactome.

## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex

## Warning in max(x, na.rm = T): no non-missing arguments to max; returning -Inf

4.2.3 Evidence plots

Figures below show the evidence plots for the top 5 gene sets. Each row corresponds to one gene set. Each column corresponds to one enrichment test (contrast + ordering). Each evidence plot shows the existing evidence for the enrichment of the given gene set in the given contrast. The curve shows the Receiver Operator Characteristic (ROC) curve for a given gene set. The rug below the figure represents the ordered list of genes. Genes belonging to a given gene set are highlighted. Colors indicate whether the genes are positively or negatively regulated (red or blue, respectively), while color brightness indicates whether genes are significantly regulated (at q < 0.05).

4.3 tmod enrichment analysis results for database Hallmark gene sets (MSigDB).

4.3.1 Summary

Database ID: msigdb_hallmark.

Description: Hallmark gene sets the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast	0.05	0.01	0.001	1e-05
NHBE_SC2V_ID0.pval	48	46	39	30
A549_SC2V_ID1.pval	41	37	35	25
A549_RSV_ID2.pval	46	44	41	36
A549_IAV_ID3.pval	42	37	32	20
A549_SC2V_vs_IAV_ID4.pval	46	44	38	29
A549_SC2V_vs_RSV_ID5.pval	46	45	42	35

4.3.2 Figure

Fig. Panel plot showing results for the database msigdb_hallmark.

4.3.3 Evidence plots

4.4 tmod enrichment analysis results for database MIR targets (MSigDB).

4.4.1 Summary

Database ID: msigdb_mir.

Description: MIR targets from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast	0.05	0.01	0.001	1e-05
NHBE_SC2V_ID0.pval	160	123	75	28
A549_SC2V_ID1.pval	172	138	89	32
A549_RSV_ID2.pval	177	150	93	51
A549_IAV_ID3.pval	178	145	108	49
A549_SC2V_vs_IAV_ID4.pval	191	164	133	84
A549_SC2V_vs_RSV_ID5.pval	196	151	109	71

4.4.2 Figure

Fig. Panel plot showing results for the database msigdb_mir.

4.4.3 Evidence plots

4.5 tmod enrichment analysis results for database GO Biological Process (MSigDB).

4.5.1 Summary

Database ID: msigdb_go_bp.

Description: GO Biological Process definitions from the Molecular Signatures DB (https://www.gsea-msigdb.org/gsea/msigdb/)..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast	0.05	0.01	0.001	1e-05
NHBE_SC2V_ID0.pval	2262	1434	913	510
A549_SC2V_ID1.pval	1434	887	583	297
A549_RSV_ID2.pval	2314	1461	962	569
A549_IAV_ID3.pval	1546	1013	653	350
A549_SC2V_vs_IAV_ID4.pval	1871	1212	793	440
A549_SC2V_vs_RSV_ID5.pval	2387	1588	1083	618

4.5.2 Figure

Fig. Panel plot showing results for the database msigdb_go_bp.

## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex

4.5.3 Evidence plots

4.6 tmod enrichment analysis results for database Co-expression gene sets (tmod).

4.6.1 Summary

Database ID: tmod.

Description: Gene sets derived from clustering expression profiles from human blood collected for various immune conditions. These gene sets are included in the tmod package by default. Check tmod documentation for further information..

Tab. Summary of the results. Numbers show the number of enrichments significant at a given threshold for the given contrast and test type.

Contrast	0.05	0.01	0.001	1e-05
NHBE_SC2V_ID0.pval	159	95	62	21
A549_SC2V_ID1.pval	134	59	34	14
A549_RSV_ID2.pval	183	105	54	23
A549_IAV_ID3.pval	138	75	41	15
A549_SC2V_vs_IAV_ID4.pval	168	90	54	10
A549_SC2V_vs_RSV_ID5.pval	203	131	82	44

4.6.2 Figure

Fig. Panel plot showing results for the database tmod.

## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex

4.6.3 Evidence plots

4.7 Cluster profiler summary overview by database

4.7.1 Overview

Table. Overview of the databases for which gene set enrichment using cluster_profiler was performed.

4.7.2 MSigDb.H

Fig. Panel plot showing results for the database MSigDb.H. Effect size is the normalized enrichment score (NES). Blue color indicates negative enrichment score, red color indicates positive NES. Size of the dots corresponds to the magnitude of NES as shown in the legend. Color intensity indicates p-value.

4.7.3 MSigDb.C2

Fig. Panel plot showing results for the database MSigDb.C2. Effect size is the normalized enrichment score (NES). Blue color indicates negative enrichment score, red color indicates positive NES. Size of the dots corresponds to the magnitude of NES as shown in the legend. Color intensity indicates p-value.

4.7.4 GO.BP

Fig. Panel plot showing results for the database GO.BP. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.

4.7.5 GO.MF

Fig. Panel plot showing results for the database GO.MF. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.

## Warning in pvalEffectPlot(me, 10^-mq, row.labels = row.labels, col.labels = col.labels, : Figure too short, the labels will overlap.
## Consider using smaller text.cex

4.7.6 KEGG.pathways

Fig. Panel plot showing results for the database KEGG.pathways. Effect size is the relative enrichment score (E) defined as (b/n)/(B/N), where b is the number of significant genes in the given gene set, n is total number of genes in the given gene set, B is the total number of significant genes and N is the total number of genes. Size of the dots corresponds to the magnitude of E as shown in the legend. Color intensity indicates p-value.

5 Session Info

## R version 3.5.1 (2018-07-02)
## Platform: x86_64-conda_cos6-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
## 
## Matrix products: default
## BLAS/LAPACK: /fast/work/users/jweiner_m/miniconda3/envs/sea_snap/lib/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_GB.UTF-8       LC_NUMERIC=C               LC_TIME=en_GB.UTF-8        LC_COLLATE=en_GB.UTF-8     LC_MONETARY=en_GB.UTF-8    LC_MESSAGES=en_GB.UTF-8   
##  [7] LC_PAPER=en_GB.UTF-8       LC_NAME=C                  LC_ADDRESS=C               LC_TELEPHONE=C             LC_MEASUREMENT=en_GB.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
##  [1] orthomapper_0.0.0.9000      tmod_0.43                   enrichplot_1.2.0            glue_1.3.1                  pander_0.6.3               
##  [6] forcats_0.4.0               stringr_1.4.0               dplyr_0.8.3                 purrr_0.3.3                 readr_1.3.1                
## [11] tidyr_1.0.0                 tibble_2.1.3                ggplot2_3.2.1               tidyverse_1.3.0             magrittr_1.5               
## [16] DT_0.11                     yaml_2.2.0                  DESeq2_1.22.1               SummarizedExperiment_1.12.0 DelayedArray_0.8.0         
## [21] BiocParallel_1.16.6         matrixStats_0.55.0          Biobase_2.42.0              GenomicRanges_1.34.0        GenomeInfoDb_1.18.1        
## [26] IRanges_2.16.0              S4Vectors_0.20.1            BiocGenerics_0.28.0         colorout_1.2-2             
## 
## loaded via a namespace (and not attached):
##   [1] readxl_1.3.1           backports_1.1.5        fastmatch_1.1-0        Hmisc_4.2-0            plyr_1.8.4             igraph_1.2.4.2         lazyeval_0.2.2        
##   [8] splines_3.5.1          crosstalk_1.0.0        urltools_1.7.3         digest_0.6.23          plotwidgets_0.4        htmltools_0.4.0        GOSemSim_2.8.0        
##  [15] viridis_0.5.1          GO.db_3.7.0            checkmate_1.9.4        memoise_1.1.0          cluster_2.1.0          limma_3.38.3           graphlayouts_0.5.0    
##  [22] annotate_1.60.1        modelr_0.1.5           prettyunits_1.0.2      colorspace_1.4-1       ggrepel_0.8.1          blob_1.2.0             rvest_0.3.5           
##  [29] haven_2.2.0            xfun_0.11              tagcloud_0.6           crayon_1.3.4           RCurl_1.95-4.12        jsonlite_1.6           genefilter_1.64.0     
##  [36] zeallot_0.1.0          survival_2.44-1.1      polyclip_1.10-0        gtable_0.3.0           zlibbioc_1.28.0        XVector_0.22.0         UpSetR_1.4.0          
##  [43] DOSE_3.8.2             scales_1.1.0           pheatmap_1.0.12        vsn_3.50.0             DBI_1.0.0              Rcpp_1.0.3             progress_1.2.2        
##  [50] viridisLite_0.3.0      xtable_1.8-4           htmlTable_1.13.2       gridGraphics_0.4-1     europepmc_0.3          foreign_0.8-72         bit_1.1-14            
##  [57] preprocessCore_1.44.0  Formula_1.2-3          htmlwidgets_1.5.1      httr_1.4.1             fgsea_1.8.0            RColorBrewer_1.1-2     acepack_1.4.1         
##  [64] ellipsis_0.3.0         pkgconfig_2.0.3        XML_3.98-1.20          farver_2.0.1           nnet_7.3-12            dbplyr_1.4.2           locfit_1.5-9.1        
##  [71] reshape2_1.4.3         ggplotify_0.0.4        tidyselect_0.2.5       labeling_0.3           rlang_0.4.2            later_1.0.0            AnnotationDbi_1.44.0  
##  [78] munsell_0.5.0          cellranger_1.1.0       tools_3.5.1            cli_1.1.0              generics_0.0.2         RSQLite_2.1.2          ggridges_0.5.2        
##  [85] broom_0.5.3            evaluate_0.14          fastmap_1.0.1          knitr_1.26             bit64_0.9-7            fs_1.3.1               tidygraph_1.1.2       
##  [92] ggraph_2.0.0           nlme_3.1-141           mime_0.7               DO.db_2.9              xml2_1.2.2             compiler_3.5.1         rstudioapi_0.10       
##  [99] beeswarm_0.2.3         affyio_1.52.0          reprex_0.3.0           tweenr_1.0.1           geneplotter_1.60.0     stringi_1.4.3          lattice_0.20-38       
## [106] Matrix_1.2-17          vctrs_0.2.0            pillar_1.4.2           lifecycle_0.1.0        BiocManager_1.30.10    triebeard_0.3.0        cowplot_1.0.0         
## [113] data.table_1.11.6      bitops_1.0-6           qvalue_2.14.1          httpuv_1.5.2           R6_2.4.1               latticeExtra_0.6-28    affy_1.60.0           
## [120] promises_1.1.0         gridExtra_2.3          MASS_7.3-51.4          assertthat_0.2.1       withr_2.1.2            GenomeInfoDbData_1.2.1 hms_0.5.2             
## [127] grid_3.5.1             rpart_4.1-15           rmarkdown_2.0          rvcheck_0.1.7          Cairo_1.5-10           ggforce_0.3.1          shiny_1.4.0           
## [134] lubridate_1.7.4        base64enc_0.1-3

DE Pipeline Report

Last update: 06 May, 2020

1 Processed data

2 Normalisation quality control metrics

2.1 Principal component analysis

2.2 Hierarchical clustering

2.3 Sample similarity

2.4 Normalised expression densities

2.5 DESeq2 normalisation

2.6 Cox outliers

3 Contrasts

3.1 NHBE_SC2V

3.1.1 MA plot

3.1.2 Results table

3.1.3 tmod enrichment analysis for NHBE_SC2V

msigdb_reactome.pval

msigdb_hallmark.pval

msigdb_mir.pval

msigdb_go_bp.pval

tmod.pval

3.1.4 cluster profiler results

3.1.4.1 Dot plot

MSigDb

H

C2

GO

BP

MF

KEGG

pathways

3.1.4.2 Enrichment map

MSigDb

H

C2

GO

BP

MF

KEGG

pathways

3.1.4.3 UpSet plot

MSigDb

H

C2

GO

BP

MF

KEGG

pathways

3.2 A549_SC2V

3.2.1 MA plot

3.2.2 Results table

3.2.3 tmod enrichment analysis for A549_SC2V

msigdb_reactome.pval

msigdb_hallmark.pval

msigdb_mir.pval

msigdb_go_bp.pval

tmod.pval

3.2.4 cluster profiler results

3.2.4.1 Dot plot

MSigDb

H

C2

GO

BP

MF

KEGG

pathways

3.2.4.2 Enrichment map

MSigDb

H

C2

GO

BP

MF

KEGG

pathways

3.2.4.3 UpSet plot

MSigDb

H

C2

3.1.3 `tmod` enrichment analysis for NHBE_SC2V

3.2.3 `tmod` enrichment analysis for A549_SC2V

3.3.3 `tmod` enrichment analysis for A549_RSV

3.4.3 `tmod` enrichment analysis for A549_IAV